Analyzing the impact of COVID-19 on consumption behaviors through recession and recovery patterns

The COVID-19 outbreak has dramatically impacted the economy, particularly consumption behaviors. Studies on how consumption responses to COVID-19 can be a powerful aid for urban consumption recovery. In this paper, based on a high-frequency consumption dataset from January 6, 2020, to April 28, 2020 covering 18 sectors and dataset from the corresponding lunar period in 2021, we look at how COVID-19 changed how people spent their money by looking at patterns of recession and recovery during the pandemic. Specifically, we first explore the recession-recovery pattern of national consumption and the effects of various policies and quantify it using regression methods. Then, recession-recovery patterns across cities are widely studied. We also reveal how consumption structures change during a pandemic and the relationship between patterns of change in citizens’ consumption and the socioeconomic characteristics of cities. And the specific empirical analysis is provided through panel regression models. In general, national consumption represented a Vshaped pattern during the pandemic, experiencing a dramatic decline and a rapid rebound. Consumption is significantly inhibited by lockdown, while it is stimulated positively but gradually by easing policies. Consumption patterns at the city level are associated with socioeconomic characteristics. Cities with high-income groups experience a more significant decline, and cities with a high share of the secondary sector have a higher recovery rate in consumption. The consumption structure redistributes but does not fundamentally change. During the recession and early recovery phase, consumption related to basic living saw a significant rise, whereas leisure-related consumption dropped dramatically and recovered slowly. Our study can assist policymakers in implementing diversified market provisions and targeted lockdown policy adjustments for consumption recovery in cities with different socioeconomic backgrounds.

Our data allows us to measure the impact of the COVID-19 pandemic on consumption behaviors from two perspectives: the consumption expenditure as well as the volume of consumption orders.Previous analyses were all based on consumer expenditure to examine the change patterns of residents' consumption during the pandemic, including detailed change of consumption across cities and sectors.To further test the results obtained earlier, we additionally provide a study of consumption orders and obtain results consistent with the study based on consumption expenditures (Supplementary Figures 3-6).
As shown in Supplementary Figures 3(a) and 3(b), consumption orders of residents show an apparent v-shaped recession and recovery pattern with three distinct phases in terms of aggregate national statistics, which consistent with the trend of national consumption expenditure.
In Supplementary Figure 4(a), we investigate the recession and recovery rate of consumption orders in 53 cities. Cities with a larger number of infected COVID-19 cases saw a much faster rate of consumption decline, as the pandemic outbreak led to a significant drop in volume of consumption orders.Likewise, recession and recovery rates of consumption orders are linearly and positively correlated with one another.And the recovery rate is associated with differences in city-level economic structure (Supplementary Figure 4(b)).
We explore the dynamics of the share of consumption across sectors during the pandemic, both for the country and for cities with different economic structures.Then we observed similar change patterns of consumption structure in terms of consumption orders (Supplementary Figure 5 and 6).

Description of Data Sectors and Validity
The raw consumption records are shown in Supplementary Table 7. Residents' consumption expenditure refers to the regular and repeated consumption expenditure in the daily life of residents for the purpose of satisfying their own needs and those of 17/26 Supplementary Table 1.Panel regression of daily year-over-year growth index (YGI) of urban residents' consumption expenditure in recession phase.Column (2) add a interaction term between socioeconomic variables with nationwide infected cases of COVID-19.Column (3) controls for city fixed effects.Standard errors are clustered at the city level.Consumption data from January 6 to February 9, 2020 and the same period in 2019 (lunar calendar).

Dependent variable
Year-over-year growth index (YGI)

Independent variable
( 0.487 0.579 0.647 ***p < 0.01; **p < 0.05 their family members.According to the residents' daily needs, the National Bureau of Statistics has divided the eight sectors of daily household expenditure into: leisure, life, living, dining, education, medicine, car, and others.In order to facilitate the study of the dynamics of the consumption structure, we divided the 18 sectors of consumption in Meituan into the eight sectors mentioned above.Auxiliary information on consumption sectors is also provided, including first-level sector name, second-level sector name, and proportion (Supplementary Table 8).
We next compare consumption data for each sector from Meituan with the national account household consumption series (Per Capita Expenditures on Consumption of Urban Households) for each corresponding sector in 2020.Specifically, we calculated the Pearson correlation coefficient between state-level consumption of our data and state-level national accounts household consumption for each quarter in 2020 from the temporal dimension, which is taken as the temporal correlation of consumption across each sector.
Due to the scarcity of national accounts data in the temporal dimension, we further verify our data in the spatial dimension.Likewise, we calculated the pearson correlation coefficient between city-level consumption of our data and city-level national accounts consumption in 2020 from the spatial dimension, taking them as the spatial correlation of consumption across each sector.The correlations are higher in the spatial dimension than in the temporal dimension, though all coefficient values are larger than 0.56 (Supplementary Table 8).This is probably explained by the more extensive data on national consumption at the city level, which allows us to obtain a more accurate correlation.
The conclusion is that our data captures important patterns across space and time in national accounts data, making it possible to act as an informative proxy along comparable cuts of official data.